A CRM Analytics consultant has prepared a CSV file to be uploaded to CRM Analytics. By mistake, one of the column headers is modified as random non-alphanumeric characters "*&**(&*(%", which went unnoticed prior to uploading the file.
What is the expected behavior of the uploaded CSV column?
When uploading CSV files into CRM Analytics, column headers must follow certain formatting rules. Headers containing non-alphanumeric characters, such as '&**(&(%', will automatically be adjusted. Specifically, if the column header starts with non-alphanumeric characters or contains such characters, CRM Analytics will prefix the header with 'X' to ensure compatibility with internal naming conventions. This behavior ensures that the column can be referenced in the platform without causing errors or conflicts.
Universal Containers has a dashboard for sales managers that want to visualize their win rate.
Which chart type should the consultant use to keep track of targets?
Cloud Kicks has a dashboard that displays accounts and opportunities data in a table that contains actions to open the records in Salesforce. Since the company has allowed several accounts to be
created with the same names, when users try to perform actions, they are prompted with only a record ID to select, leaving the users confused and unable to act.
How should the CRM Analytics consultant solve this problem?
What is the purpose of the CRM Analytics Dashboard Inspector?
The CRM Analytics Dashboard Inspector is a powerful tool used to troubleshoot and optimize dashboards. Its primary function is to display the underlying SAQL (Salesforce Analytics Query Language) query executed for each widget. It helps users see the final query that is run and the corresponding results. This feature allows CRM Analytics consultants and developers to diagnose issues, optimize performance, and understand how data is being processed in the dashboard.
While the Inspector helps view execution times and identify bottlenecks, it does not automatically resolve performance issues (which is why option B is incorrect). It simply provides visibility into query performance and execution details, allowing the user to make manual optimizations.
Cloud Kicks (CK) wants to use CRM Analytics to analyze trends of its sales pipeline in order to accelerate the company's sales process. To do so, CK needs to know the average time an opportunity
spends in each stage. The data can be found in the Opportunity History object, but the value is not pre-calculated in Salesforce, so a consultant recommends using a recipe to calculate it.
How should the consultant use a recipe to calculate the average time an opportunity spends in each stage?
